Although machine translation systems are mostly designed to serve in the general domain, there is a growing tendency to adapt these systems to other domains like literary translation. In this paper, we focus on English-Turkish literary translation and develop machine translation models that take into account the stylistic features of translators. We fine-tune a pre-trained machine translation model by the manually-aligned works of a particular translator. We make a detailed analysis of the effects of manual and automatic alignments, data augmentation methods, and corpus size on the translations. We propose an approach based on stylistic features to evaluate the style of a translator in the output translations. We show that the human translator style can be highly recreated in the target machine translations by adapting the models to the style of the translator.

翻译：尽管机器翻译系统主要设计用于通用领域，但将此类系统适配至文学翻译等特定领域的趋势日益增长。本文聚焦英土文学翻译领域，开发了能考虑译者风格特征的机器翻译模型。我们通过特定译者的人工对齐作品对预训练机器翻译模型进行微调，详细分析了人工对齐与自动对齐、数据增强方法及语料库规模对翻译效果的影响，并提出一种基于风格特征的评估方法以衡量输出译文中译者的风格表现。研究表明，通过使模型适配译者的风格，能够在目标机器翻译中高度再现人类译者的风格特征。